ML Tasks

View AI supports 3 model tasks as per now. These include:

• Binary Classification

• Multi-class Classification

• Regression

Binary classification is the task of classifying the elements of an outcome set into two groups (each called class) on the basis of a classification rule.

Onboarding a Binary classification task in View AI requires the following:

• A single output column of type float (range 0-1) which represents the soft output of the model. This column has to be defined.

• A single target column that represents the true outcome. This column has to be defined.

• A list of input features has to be defined.

Typical binary classification problems include:

• Determining whether a customer will churn or not. Here the outcome set has two outcomes: The customer will churn or the customer will not. Further, the outcome can only belong to either of the two classes.

• Determining whether a patient has a disease or not. Here the outcome set has two outcomes: the patient has the disease or does not.

Multiclass classification is the task of classifying the elements of an outcome set into three or more groups (each called class) on the basis of a classification rule.

Onboarding a Multiclass classification task in View AI requires the following:

• Multiple output columns (one per class) of type float (range 0-1) which represent the soft outputs of the model. Those columns have to be defined.

• A single target column that represents the true outcome. This column has to be defined.

• A list of input features has to be defined.

Typical multiclass classification problems include:

• Determining whether an image is a cat, a dog, or a bird. Here the outcome set has more than two outcomes. Further, the image can only be determined to be one of the three outcomes and it's thus a multiclass classification problem.

Regression is the task of predicting a continuous numeric quantity.

Onboarding a Regression task in View AI requires the following:

• A single numeric output column that represents the output of the model. This column has to be defined.

• A single numeric target column that represents the true outcome. This column has to be defined.

• A list of input features has to be defined.

Typical regression problems include:

• Determining the average home price based on a given set of housing-related features such as its square footage, number of beds and baths, its location, etc.

• Determining the income of an individual based on features such as age, work location, job sector, etc.

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